System and Method for Area Coverage Using Sector Decomposition

ABSTRACT

The different illustrative embodiments provide a method for generating an area coverage path plan using sector decomposition. A starting point is identified on a worksite map having a number of landmarks. A first landmark in the number of landmarks is identified. A path is generated around the first landmark until an obstacle is detected. In response to detecting the obstacle, the path is made linear to a next landmark. The path is generated around the next landmark.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of and claims the benefit of priorityto U.S. patent application Ser. No. 12/640,845, filed on Dec. 17, 2009and entitled “System and Method for Area Coverage Using SectorDecomposition”.

This application is also related to commonly assigned U.S. patentapplication Ser. No. 12/640,937, filed on Dec. 17, 2009 and entitled“System and Method for Deploying Portable Landmarks”; and U.S. patentapplication Ser. No. 12/640,953, filed on Dec. 17, 2009 and entitled“Enhanced Visual Landmark for Localization” all of which are herebyincorporated by reference.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods fornavigation and more particularly to systems and methods for mobilerobotic navigation. Still more specifically, the present disclosurerelates to a method and system for area coverage using sectordecomposition.

BACKGROUND OF THE INVENTION

The use of robotic devices to perform physical tasks has increased inrecent years. Mobile robotic devices can be used to perform a variety ofdifferent tasks. These mobile devices may operate in semi-autonomous orfully autonomous modes. Some robotic devices are constrained to operatein a contained area, using different methods to obtain coverage withinthe contained area. These robotic devices typically have an integrated,fixed positioning and navigation system. Mobile robotic devices oftenrely on dead reckoning or use of a global positioning system to achievearea coverage. These systems tend to be inefficient and are oftencost-prohibitive.

SUMMARY

The different illustrative embodiments provide a method for generatingan area coverage path plan using sector decomposition. A starting pointis identified on a worksite map having a number of landmarks. A firstlandmark in the number of landmarks is identified. A path is generatedaround the first landmark until an obstacle is detected. In response todetecting the obstacle, the path is made linear to a next landmark. Thepath is generated around the next landmark.

The different illustrative embodiments further provide a method forexecuting an area coverage path plan using sector decomposition. Anexpected width of a landmark is determined in pixels for a desireddistance away from the landmark. An image having the landmark isidentified. An observed width of the landmark is determined using theimage. The observed width of the landmark is compared to the expectedwidth of the landmark. A message is sent to a vehicle control process tomove an autonomous vehicle based on the comparison of the observed widthand the expected width.

The different illustrative embodiments further provide a method forgenerating a worksite map using simultaneous localization and mapping. Aworksite having a number of landmarks is identified. A worksite map isgenerated for the worksite. An area coverage task is initiated at theworksite. A landmark marked as unvisited is identified on the worksitemap. A message is sent to a vehicle control process to proceed to thelandmark marked as unvisited.

The features, functions, and advantages can be achieved independently invarious embodiments of the present invention, or may be combined in yetother embodiments in which further details can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrativeembodiments are set forth in the appended claims. The illustrativeembodiments, however, as well as a preferred mode of use, furtherobjectives and advantages thereof, will best be understood by referenceto the following detailed description of an illustrative embodiment ofthe present invention when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a block diagram of a worksite environment in which anillustrative embodiment may be implemented;

FIG. 2 is a block diagram of a data processing system in accordance withan illustrative embodiment;

FIG. 3 is a block diagram of a navigation system in accordance with anillustrative embodiment;

FIG. 4 is a block diagram of a mobility system in accordance with anillustrative embodiment;

FIG. 5 is a block diagram of a sensor system in accordance with anillustrative embodiment;

FIG. 6 is a block diagram of a behavior database in accordance with anillustrative embodiment;

FIG. 7 is a block diagram of a worksite database in accordance with anillustrative embodiment;

FIG. 8 is a block diagram of a worksite map in accordance with anillustrative embodiment;

FIG. 9 is a flowchart illustrating a process for executing a path planin accordance with an illustrative embodiment;

FIG. 10 is a flowchart illustrating a process for executing a path planusing simultaneous localization and mapping in accordance with anillustrative embodiment;

FIG. 11 is a flowchart illustrating a process for executing an areacoverage path plan using sector decomposition in accordance with anillustrative embodiment; and

FIG. 12 is a flowchart illustrating a process for generating an areacoverage path plan using sector decomposition in accordance with anillustrative embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference to the figures and in particular with reference to FIG.1, a block diagram of a worksite environment is depicted in which anillustrative embodiment may be implemented. Worksite environment 100 maybe any type of worksite environment in which an autonomous vehicle canoperate. In an illustrative example, worksite environment 100 may be astructure, building, worksite, area, yard, golf course, indoorenvironment, outdoor environment, different area, change in the needs ofa user, and/or any other suitable worksite environment or combination ofworksite environments.

As an illustrative example, a change in the needs of a user may include,without limitation, a user moving from an old location to a new locationand operating an autonomous vehicle in the yard of the new location,which is different than the yard of the old location. As anotherillustrative example, a different area may include, without limitation,operating an autonomous vehicle in both an indoor environment and anoutdoor environment, or operating an autonomous vehicle in a front yardand a back yard, for example.

Worksite environment 100 includes network 101 in one embodiment of thepresent invention. In this example, back office 102 may be a singlecomputer or a distributed computing cloud. Back office 102 supports thephysical databases and/or connections to external databases which may beused in the different illustrative embodiments. Back office 102 maysupply databases to different vehicles, as well as provide online accessto information from databases. Back office 102 may also provide pathplans for vehicles, such as autonomous vehicle 104, for example.Worksite environment 100 may include autonomous vehicle 104, number ofworksites 106, user 108, and manual control device 110. As used herein,a number of items means one or more items. For example, number ofworksites 106 is one or more worksites.

Autonomous vehicle 104 may be any type of autonomous vehicle including,without limitation, a mobile robotic machine, a service robot, a fieldrobot, a robotic mower, a robotic snow removal machine, a robotic leafremoval machine, a robotic lawn watering machine, a robotic vacuum,and/or any other autonomous vehicle. Autonomous vehicle 104 includesnavigation system 112. Navigation system 112 provides a base system forcontrolling the mobility, positioning, and navigation for autonomousvehicle 104. Base system capabilities may include base behaviors suchas, for example, without limitation, base mobility functions foreffectuating random area coverage of a worksite, base obstacle avoidancefunctions for contact switch obstacle avoidance, base dead reckoning forpositioning functions, and/or any other combination of basicfunctionality for autonomous vehicle 104.

Number of worksites 106 may be any area within worksite environment 100in which autonomous vehicle 104 can operate. Each worksite in number ofworksites 106 may be associated with a number of tasks. Worksite 114 isan illustrative example of one worksite in number of worksites 106. Forexample, in an illustrative embodiment, worksite 114 may be a back yardof a residence of a user. Worksite 114 includes number of tasks 116. Inan illustrative example, number of tasks 116 may include mowing the backyard of the residence of a user. Autonomous vehicle 104 may operate toperform number of tasks 116 within worksite 114. As used herein, numberrefers to one or more items. In one illustrative example, number ofworksites 106 may include, without limitation, a primary yard and asecondary yard. The primary yard may be worksite 114, associated withnumber of tasks 116. The secondary yard may be associated with anotherset of tasks, for example.

Each worksite in number of worksites 106 may include a number ofworksite areas, a number of landmarks, and/or a number of obstacles.Worksite 114 includes number of worksite areas 118, number of landmarks120, and number of obstacles 122. In an illustrative example, number ofworksite areas 118 may be a number of locations within worksite 114,such as, for example, without limitation, a starting point, a midpoint,and an ending point. In another illustrative example, number of worksiteareas 118 may include a sub-area of worksite 114.

Number of landmarks 120 may be any type of feature capable of beingdetected by autonomous vehicle 104 and used for identifying a locationof a worksite. In an illustrative example, number of landmarks 120 mayinclude, without limitation, cylindrical landmarks, colored landmarks,patterned landmarks, illuminated landmarks, vertical landmarks, naturallandmarks, any combination of the foregoing, and/or any other suitablelandmark. Patterned landmarks may include a visual pattern incorporatedto provide distinctive information, for example. Illuminated landmarksmay provide visual detection in low-light or no-light situations, suchas night time, for example. Natural landmarks may include, for example,without limitation, tree trunks. Other types of landmarks may include,for example, building architectural features, driveways, sidewalks,curbs, fences, and/or any other suitable landmarks.

Number of obstacles 122 may be any type of object that occupies aphysical space within worksite 114 and/or a location that autonomousvehicle 104 should not occupy or cross. The types of objects that occupya physical space within worksite 114 may refer to objects that may bedamaged by or cause damage to autonomous vehicle 104 if they were tocontact each other, particularly with non-zero speed, for example. Thelocations which autonomous vehicle 104 should not occupy or should notcross may be independent of what occupies that space or is on the otherside of the boundary, for example.

User 108 may be, without limitation, a human operator, a roboticoperator, or some other external system. Manual control device 110 maybe any type of manual controller, which allows user 108 to overrideautonomous behaviors and control autonomous vehicle 104. In anillustrative example, user 108 may use manual control device 110 tocontrol movement of autonomous vehicle 104 from home location 124 toworksite 114 in order to perform number of tasks 116.

Home location 124 may be a docking station or storage station forautonomous vehicle 104. Home location 124 may include power supply 126.Power supply 126 may provide power to autonomous vehicle 104 whenautonomous vehicle 104 is at home location 124. In an illustrativeexample, power supply 126 may recharge a power store or power supply ofautonomous vehicle 104. Power supply 126 may include, withoutlimitation, a battery, mobile battery recharger, ultracapacitor, fuelcell, gas powered generator, photo cells, and/or any other suitablepower source.

The illustration of worksite environment 100 in FIG. 1 is not meant toimply physical or architectural limitations to the manner in whichdifferent advantageous embodiments may be implemented. Other componentsin addition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

The different illustrative embodiments recognize and take into accountthat currently used methods for robotic navigation often use a veryprimitive, random navigation system. This random navigation system workswithin a perimeter established by a wire carrying an electrical signal.The robotic machines in currently used methods may be equipped with anelectrical signal detector and a bumper switch on the body of themachine. These machines move in a generally straight direction untilthey either detect the signal from the perimeter wire, or a bumperswitch is closed due to contact of the machine with an external object.When either of these two situations occurs, these machines changedirection. In this way, current methods constrain the machine within awork area perimeter and maintain movement after contact with externalobjects.

The different illustrative embodiments further recognize and take intoaccount that currently used systems for robotic navigation are fixedsystems integrated into a robotic machine. These fixed systems mayinclude sensors for positioning and navigation, which allows for moreefficient and precise coverage, but also increases the expense of therobotic machine by hundreds or thousands of dollars above the price of arobotic machine with basic, random navigation systems.

The different illustrative embodiments further recognize and take intoaccount that currently used methods for robotic navigation raiseconcerns for consumers when considering whether to move from manned tounmanned machines. Consumers may wonder if the lower cost, yet randomcoverage ability of some machines will meet aesthetic standards for themachine task. Another concern may be the capability of a machine to workadequately in one environment over another environment. Still anotherconcern may be continual technology updates and the cost of having toreplace an entire machine when the fixed navigation systems in currentmachines become obsolete.

Thus, one or more of the different illustrative embodiments provide amethod for generating an area coverage path plan using sectordecomposition. A starting point is identified on a worksite map having anumber of landmarks. A first landmark in the number of landmarks isidentified. A path is generated around the first landmark until anobstacle is detected. In response to detecting the obstacle, the path ismade linear to a next landmark. The path is generated around the nextlandmark.

The different illustrative embodiments further provide a method forexecuting an area coverage path plan using sector decomposition. Anexpected width of a landmark is determined in pixels for a desireddistance away from the landmark. An image having the landmark isidentified. An observed width of the landmark is determined using theimage. The observed width of the landmark is compared to the expectedwidth of the landmark. A message is sent to a vehicle control process tomove an autonomous vehicle based on the comparison of the observed widthand the expected width.

The different illustrative embodiments further provide a method forgenerating a worksite map using simultaneous localization and mapping. Aworksite having a number of landmarks is identified. A worksite map isgenerated for the worksite. An area coverage task is initiated at theworksite. A landmark marked as unvisited is identified on the worksitemap. A message is sent to a vehicle control process to proceed to thelandmark marked as unvisited.

The different illustrative embodiments provide the ability toefficiently cover an area for an automated task without the high costand environmental limitations of existing high precision localizationsystems. The different illustrative embodiments further provide theability to cover an area with the excessive wear experienced by asemi-random area coverage method.

With reference now to FIG. 2, a block diagram of a data processingsystem is depicted in accordance with an illustrative embodiment. Dataprocessing system 200 is an example of a computer, such as back office102 in FIG. 1, in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

In this illustrative example, data processing system 200 includescommunications fabric 202, which provides communications betweenprocessor unit 204, memory 206, persistent storage 208, communicationsunit 210, input/output (I/O) unit 212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices216. A storage device is any piece of hardware that is capable ofstoring information, such as, for example without limitation, data,program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. Memory 206, inthese examples, may be, for example, a random access memory or any othersuitable volatile or non-volatile storage device. Persistent storage 208may take various forms depending on the particular implementation. Forexample, persistent storage 208 may contain one or more components ordevices. For example, persistent storage 208 may be a hard drive, aflash memory, a rewritable optical disk, a rewritable magnetic tape, orsome combination of the above. The media used by persistent storage 208also may be removable. For example, a removable hard drive may be usedfor persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 212 may send output to a printer. Display 214provides a mechanism to display information to a user.

Instructions for the operating system, applications and/or programs maybe located in storage devices 216, which are in communication withprocessor unit 204 through communications fabric 202. In theseillustrative examples, the instructions are in a functional form onpersistent storage 208. These instructions may be loaded into memory 206for execution by processor unit 204. The processes of the differentembodiments may be performed by processor unit 204 using computerimplemented instructions, which may be located in a memory, such asmemory 206.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 204. The program code in thedifferent embodiments may be embodied on different physical or tangiblecomputer readable media, such as memory 206 or persistent storage 208.

Program code 218 is located in a functional form on computer readablemedia 220 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 218 and computer readable media 220 form computerprogram product 222 in these examples. In one example, computer readablemedia 220 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 220 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 220 is also referred to as computerrecordable storage media. In some instances, computer readable media 220may not be removable.

Alternatively, program code 218 may be transferred to data processingsystem 200 from computer readable media 220 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

In some illustrative embodiments, program code 218 may be downloadedover a network to persistent storage 208 from another device or dataprocessing system for use within data processing system 200. Forinstance, program code stored in a computer readable storage medium in aserver data processing system may be downloaded over a network from theserver to data processing system 200. The data processing systemproviding program code 218 may be a server computer, a client computer,or some other device capable of storing and transmitting program code218.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of executingprogram code. As one example, the data processing system may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

As another example, a storage device in data processing system 200 isany hardware apparatus that may store data. Memory 206, persistentstorage 208 and computer readable media 220 are examples of storagedevices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

As used herein, the phrase “at least one of”, when used with a list ofitems, means that different combinations of one or more of the items maybe used and only one of each item in the list may be needed. Forexample, “at least one of item A, item B, and item C” may include, forexample, without limitation, item A or item A and item B. This examplealso may include item A, item B, and item C or item B and item C.

With reference now to FIG. 3, a block diagram of a navigation system isdepicted in accordance with an illustrative embodiment. Navigationsystem 300 is an example of one implementation of navigation system 112in FIG. 1.

Navigation system 300 includes processor unit 302, communications unit304, behavior database 306, worksite database 308, mobility system 310,sensor system 312, power supply 314, power level indicator 316, basesystem interface 318, and vision system 320. Vision system 320 includesnumber of cameras 322. Number of cameras 322 may include, for example,without limitation, a color camera, a black and white camera, a digitalcamera, an infrared camera, and/or any other suitable camera.

In one illustrative example, number of cameras 322 may be oriented tocapture a view that is down and horizontal relative to the autonomousvehicle associated with navigation system 300, such as autonomousvehicle 104 in FIG. 1, for example. In this illustrative example, theorientation of number of cameras 322 may enable autonomous vehiclebehaviors, such as boundary and/or perimeter following, for example, inaddition to landmark identification and localization. In an illustrativeexample where number of cameras 322 includes a color camera, boundaryfollowing behaviors may use number of cameras 322 to identify a colorboundary, such as green grass contrasted with a concrete curb, forexample.

In another illustrative example, number of cameras 322 may be orientedto capture a view facing perpendicular to the direction of travel of theautonomous vehicle associated with navigation system 300, such asautonomous vehicle 104 in FIG. 1, for example. In yet anotherillustrative example, number of cameras 322 may be oriented to capture aview facing the landmark that the autonomous vehicle associated withnavigation system 300 is traveling around, for example.

Vision system 320 operates to provide depth of field perception byproviding number of images 324 from number of cameras 322, for enhancedvision capabilities of navigation system 300. Vision system 320 may be,for example, without limitation, a stereo vision system, an asymmetricvision system, a stadiametric ranging vision system, and/or any othersuitable vision system. Number of cameras 322 may be used to capturenumber of images 324 of a worksite or worksite area, such as worksite114 in FIG. 1, for example. Number of images 324 may be transferred overbase system interface 318 to processor unit 302 for use in landmarkidentification and path planning, for example. As used herein, “numberof” refers to one or more images.

Processor unit 302 may be an example of one implementation of dataprocessing system 200 in FIG. 2. Processor unit 302 includes vehiclecontrol process 326. Vehicle control process 326 is configured tocommunicate with and control mobility system 310. Vehicle controlprocess 326 includes path planning module 328. Path planning module 328may use information from behavior database 306 and worksite database308, along with number of images 324 received from vision system 320, togenerate path plan 330. Path planning module 328 may generate path plan330 using sector decomposition process 332 to plan a path for aworksite, for example. A path may be any length, for example one foot orten feet, and may change as the position of the autonomous vehiclerelative to a landmark, obstacle, perimeter, and/or boundary changes.Sector decomposition process 332 is an area coverage algorithm, as shownin more illustrative detail in FIGS. 8 and 13. Sector decompositionprocess 332 may enable path planning module 328 and/or vehicle controlprocess 326 to plan and execute path plan 330 with only one visiblelandmark at any given location of a worksite, for example. Sectordecomposition process 332 generates paths which follow arcs atpredefined distances from landmarks. The predefined distances may be,for example, without limitation, equal to the width of an autonomousvehicle, equal to the task coverage width for one pass of an autonomousvehicle, and/or any other specified distance. In one illustrativeexample, sector decomposition process 332 may generate paths with arcsthat are progressively closer together as the autonomous vehicleproceeds further away from a landmark in order to compensate forsite-specific error. Sector decomposition process 332 may also generatelinear paths for point-to-point behaviors in order to move an autonomousvehicle from one landmark to another landmark, for example.

In an illustrative example, path planning module 328 may retrieve aworksite map from worksite database 308 in order to plan a path, such aspath plan 330, for the worksite. A worksite map is a map that identifiesa worksite, such as worksite 114 in FIG. 1, for example. A worksite mapmay be used to identify a location for an area coverage task and plan apath for execution of the area coverage task on a worksite. The worksitemap may have a number of landmarks identified in this example. Vehiclecontrol process 326 may use path plan 330 to send commands and/orsignals to mobility system 310 in order to move an autonomous vehicleassociated with navigation system 300 according to path plan 330.Vehicle control process 326 may initiate an area coverage task usingpath plan 330 in response to a trigger, such as, for example, withoutlimitation, a button being selected on an autonomous vehicle, a commandfrom a manual control device, a software-driven event, a time-drivenevent, and/or any other suitable trigger.

Processor unit 302 may also include simultaneous localization andmapping process 334, as shown in more illustrative detail in FIGS. 9 and10. Simultaneous localization and mapping process 334 may generate aworksite map having a path plan, such as path plan 330, during operationof an area coverage task by the autonomous vehicle associated withnavigation system 300, for example.

Processor unit 302 may further communicate with and access data storedin behavior database 306 and worksite database 308. Accessing data mayinclude any process for storing, retrieving, and/or acting on data inbehavior database 306 and/or worksite database 308. For example,accessing data may include, without limitation, using a lookup tablehoused in behavior database 306 and/or worksite database 308, running aquery process using behavior database 306 and/or worksite database 308,and/or any other suitable process for accessing data stored in adatabase.

Processor unit 302 receives information from sensor system 312 and mayuse sensor information in conjunction with behavior data from behaviordatabase 306 when controlling mobility system 310. Processor unit 302may also receive control signals from an outside controller, such asmanual control device 110 operated by user 108 in FIG. 1, for example.These control signals may be received by processor unit 302 usingcommunications unit 304.

Communications unit 304 may provide communications links to processorunit 302 to receive information. This information includes, for example,data, commands, and/or instructions. Communications unit 304 may takevarious forms. For example, communications unit 304 may include awireless communications system, such as a cellular phone system, a Wi-Fiwireless system, or some other suitable wireless communications system.

Communications unit 304 may also include a wired connection to anoptional manual controller, such as manual control device 110 in FIG. 1,for example. Further, communications unit 304 also may include acommunications port, such as, for example, a universal serial bus port,a serial interface, a parallel port interface, a network interface, orsome other suitable port to provide a physical communications link.Communications unit 304 may be used to communicate with an externalcontrol device or user, for example.

In one illustrative example, processor unit 302 may receive controlsignals from manual control device 110 operated by user 108 in FIG. 1.These control signals may override autonomous behaviors of vehiclecontrol process 326 and allow user 108 to stop, start, steer, and/orotherwise control the autonomous vehicle associated with navigationsystem 300.

Behavior database 306 contains a number of behavioral actions whichvehicle control process 326 may utilize when controlling mobility system310. Behavior database 306 may include, without limitation, basicvehicle behaviors, area coverage behaviors, perimeter behaviors,obstacle avoidance behaviors, manual control behaviors, power supplybehaviors, and/or any other suitable behaviors for an autonomousvehicle.

Mobility system 310 provides mobility for an autonomous vehicle, such asautonomous vehicle 104 in FIG. 1. Mobility system 310 may take variousforms. Mobility system 310 may include, for example, without limitation,a propulsion system, steering system, braking system, and mobilitycomponents. In these examples, mobility system 310 may receive commandsfrom vehicle control process 326 and move an associated autonomousvehicle in response to those commands.

Sensor system 312 may include a number of sensor systems for collectingand transmitting sensor data to processor unit 302. For example, sensorsystem 312 may include, without limitation, a dead reckoning system, anobstacle detection system, a perimeter detection system, and/or someother suitable type of sensor system, as shown in more illustrativedetail in FIG. 5. Sensor data is information collected by sensor system312.

Power supply 314 provides power to components of navigation system 300and the associated autonomous vehicle, such as autonomous vehicle 104 inFIG. 1, for example. Power supply 314 may include, without limitation, abattery, mobile battery recharger, ultracapacitor, fuel cell, gaspowered generator, photo cells, and/or any other suitable power source.Power level indicator 316 monitors the level of power supply 314 andcommunicates the power supply level to processor unit 302. In anillustrative example, power level indicator 316 may send informationabout a low level of power in power supply 314. Processor unit 302 mayaccess behavior database 306 to employ a behavioral action in responseto the indication of a low power level, in this illustrative example.For example, without limitation, a behavioral action may be to ceaseoperation of a task and seek a recharging station in response to thedetection of a low power level.

Base system interface 318 provides power and data communications betweenvision system 320 and the other components of navigation system 300. Inan illustrative example, number of images 324 may be transferred toprocessor unit 302 from vision system 320 using base system interface318.

The illustration of navigation system 300 in FIG. 3 is not meant toimply physical or architectural limitations to the manner in whichdifferent advantageous embodiments may be implemented. Other componentsin addition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

For example, in one advantageous embodiment, number of cameras 322 maycomprise two cameras. The first camera may be sufficient to implementsector decomposition process 332, while the second camera providesadditional capabilities to the autonomous vehicle associated withnavigation system 300. In this illustrative example, the second cameramay be used for operation in an extended range from a landmark. Themaximum distance at which a landmark may be used by navigation system300 may be a function of a maximum error specified for path following,camera resolution, camera field of view, and landmark width, forexample. When vision system 320 of navigation system 300 is at theclosest specified point to a landmark, the landmark may fill the imagecaptured by the first camera. As vision system 320 is moved further awayfrom the landmark by the autonomous vehicle associated with navigationsystem 300, edge errors from image acquisition increase as the edgebecomes a larger percentage of the landmark width in the image. Adding asecond camera with a narrower field of view than the first camera, inthis illustrative example, may allow the landmark to occupy more of theprocessed image at a further distance from the landmark than the firstcamera allowed, minimizing edge errors, for example.

In another illustrative example, an additional camera may be used forstereo vision behaviors, useful for behaviors such as circle obstacle360 degrees 626 and circle obstacle 180 degrees 624 in FIG. 6, forexample. In yet another illustrative example, an additional camera maybe used to allow vision system 320 to capture number of images 324 onmore than one side of an autonomous vehicle associated with navigationsystem 300. For example, number of cameras 322 may face views onopposite sides of the autonomous vehicle, providing simplified arctransitions without the need for the autonomous vehicle to position asingle camera to continually face a landmark.

Any number of additional cameras may be added to number of cameras 322.As used herein, “number of cameras” refers to one or more cameras.

With reference now to FIG. 4, a block diagram of a mobility system isdepicted in accordance with an illustrative embodiment. Mobility system400 is an example of one implementation of mobility system 310 in FIG.3.

Mobility system 400 provides mobility for autonomous vehicles associatedwith a navigation system, such as navigation system 300 in FIG. 3.Mobility system 400 may take various forms. Mobility system 400 mayinclude, for example, without limitation, propulsion system 402,steering system 404, braking system 406, and number of mobilitycomponents 408. In these examples, propulsion system 402 may propel ormove an autonomous vehicle, such as autonomous vehicle 104 in FIG. 1, inresponse to commands from a navigation system, such as navigation system300 in FIG. 3.

Propulsion system 402 may maintain or increase the speed at which anautonomous vehicle moves in response to instructions received from aprocessor unit of a navigation system. Propulsion system 402 may be anelectrically controlled propulsion system. Propulsion system 402 may be,for example, without limitation, an internal combustion engine, aninternal combustion engine/electric hybrid system, an electric engine,or some other suitable propulsion system. In an illustrative example,propulsion system 402 may include wheel drive motors 410. Wheel drivemotors 410 may be an electric motor incorporated into a mobilitycomponent, such as a wheel, that drives the mobility component directly.In one illustrative embodiment, steering may be accomplished bydifferentially controlling wheel drive motors 410.

Steering system 404 controls the direction or steering of an autonomousvehicle in response to commands received from a processor unit of anavigation system. Steering system 404 may be, for example, withoutlimitation, an electrically controlled hydraulic steering system, anelectrically driven rack and pinion steering system, a differentialsteering system, or some other suitable steering system. In anillustrative example, steering system 404 may include a dedicated wheelconfigured to control number of mobility components 408.

Braking system 406 may slow down and/or stop an autonomous vehicle inresponse to commands received from a processor unit of a navigationsystem. Braking system 406 may be an electrically controlled brakingsystem. This braking system may be, for example, without limitation, ahydraulic braking system, a friction braking system, a regenerativebraking system using wheel drive motors 410, or some other suitablebraking system that may be electrically controlled. In one illustrativeembodiment, a navigation system may receive commands from an externalcontroller, such as manual control device 110 in FIG. 1, to activate anemergency stop. The navigation system may send commands to mobilitysystem 400 to control braking system 406 to perform the emergency stop,in this illustrative example.

Number of mobility components 408 provides autonomous vehicles with thecapability to move in a number of directions and/or locations inresponse to instructions received from a processor unit of a navigationsystem and executed by propulsion system 402, steering system 404, andbraking system 406. Number of mobility components 408 may be, forexample, without limitation, wheels, tracks, feet, rotors, propellers,wings, and/or other suitable components.

The illustration of mobility system 400 in FIG. 4 is not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

With reference now to FIG. 5, a block diagram of a sensor system isdepicted in accordance with an illustrative embodiment. Sensor system500 is an example of one implementation of sensor system 312 in FIG. 3.

Sensor system 500 includes a number of sensor systems for collecting andtransmitting sensor data to a processor unit of a navigation system,such as navigation system 300 in FIG. 3. Sensor system 500 includesobstacle detection system 502, perimeter detection system 504, and deadreckoning system 506.

Obstacle detection system 502 may include, without limitation, number ofcontact switches 508 and ultrasonic transducer 510. Number of contactswitches 508 detects contact by an autonomous vehicle with an externalobject in the environment, such as worksite environment 100 in FIG. 1,for example. Number of contact switches 508 may include, for example,without limitation, bumper switches. Ultrasonic transducer 510 generateshigh frequency sound waves and evaluates the echo received back.Ultrasonic transducer 510 calculates the time interval between sendingthe signal, or high frequency sound waves, and receiving the echo todetermine the distance to an object.

Perimeter detection system 504 detects a perimeter or boundary of aworksite, such as worksite 114 in FIG. 1, and sends information aboutthe perimeter detection to a processor unit of a navigation system.Perimeter detection system 504 may include, without limitation, receiver512 and infrared detector 514. Receiver 512 detects electrical signals,which may be emitted by a wire delineating the perimeter of a worksite,such as worksite 114 in FIG. 1, for example. Infrared detector 514detects infrared light, which may be emitted by an infrared light sourcealong the perimeter of a worksite, such as worksite 114 in FIG. 1, forexample.

In an illustrative example, receiver 512 may detect an electrical signalfrom a perimeter wire, and send information about that detected signalto a processor unit of a navigation system, such as navigation system300 in FIG. 3. The navigation system may then send commands to amobility system, such as mobility system 400 in FIG. 4, to alter thedirection or course of an autonomous vehicle associated with thenavigation system, in this illustrative example.

Dead reckoning system 506 estimates the current position of anautonomous vehicle associated with the navigation system. Dead reckoningsystem 506 estimates the current position based on a previouslydetermined position and information about the known or estimated speedover elapsed time and course. Dead reckoning system 506 may include,without limitation, odometer 516, compass 518, and accelerometer 520.Odometer 516 is an electronic or mechanical device used to indicatedistance traveled by a machine, such as autonomous vehicle 104 inFIG. 1. Compass 518 is a device used to determine position or directionrelative to the Earth's magnetic poles. Accelerometer 520 measures theacceleration it experiences relative to freefall.

The illustration of sensor system 500 in FIG. 5 is not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

With reference now to FIG. 6, a block diagram of a behavior database isdepicted in accordance with an illustrative embodiment. Behaviordatabase 600 is an example of one implementation of behavior database306 in FIG. 3.

Behavior database 600 includes a number of behavioral actions vehiclecontrol process 326 of navigation system 300 may utilize whencontrolling mobility system 310 in FIG. 3. Behavior database 600 mayinclude, without limitation, basic vehicle behaviors 602, area coveragebehaviors 604, perimeter behaviors 606, obstacle avoidance behaviors608, manual control behaviors 610, power supply behaviors 612, and/orany other suitable behaviors for an autonomous vehicle.

Basic vehicle behaviors 602 provide actions for a number of basic tasksan autonomous vehicle may perform. Basic vehicle behaviors 602 mayinclude, without limitation, mowing, vacuuming, floor scrubbing, leafremoval, snow removal, watering, spraying, security, and/or any othersuitable task.

Area coverage behaviors 604 provide actions for area coverage whenperforming basic vehicle behaviors 602. Area coverage behaviors 604 mayinclude, without limitation, sector decomposition behaviors 614. Sectordecomposition behaviors 614 may include, for example, withoutlimitation, follow arc 616, point-to-point 618, and/or any othersuitable behaviors.

Perimeter behaviors 606 provide actions for a navigation system inresponse to perimeter detection, such as by perimeter detection system504 in FIG. 5. In an illustrative example, perimeter behaviors 606 mayinclude, without limitation, follow perimeter 620, change heading 622,and/or any other suitable behaviors. Change heading 622 may operate tochange the heading for an autonomous vehicle by a number of degrees inorder to stay within a perimeter. Follow perimeter 620 may operate tomove an autonomous vehicle parallel to a perimeter for a predefineddistance. A predefined distance may be, for example, a distance equal tothe width of the autonomous vehicle less an error amount.

Obstacle avoidance behaviors 608 provide actions for a navigation systemto avoid collision with objects in an environment around an autonomousvehicle. In an illustrative example, obstacle avoidance behaviors 608may include, without limitation, circle obstacle 180 degrees 624, circleobstacle 360 degrees 626, reverse direction and change heading 628,and/or any other suitable behaviors. Circle obstacle 180 degrees 624 mayoperate to direct an autonomous vehicle half way around an obstacle tocontinue in a second direction opposite the first direction, forexample. Circle obstacle 360 degrees 626 may operate to direct anautonomous vehicle around the entirety of an obstacle in order toperform a task on all areas around the obstacle, for example. Reversedirection and change heading 628 may operate to reverse direction andchange heading of an autonomous vehicle to avoid an object detected byan obstacle detection system, such as obstacle detection system 502 inFIG. 5.

Manual control behaviors 610 provide actions for a navigation system todisable autonomy and take motion control from a user, such as user 108in FIG. 1, for example. Power supply behaviors 612 provide actions for anavigation system to take a number of actions in response to a detectedlevel of power in a power supply, such as power supply 314 in FIG. 3. Inan illustrative example, power supply behaviors 612 may include, withoutlimitation, stopping the task operation of an autonomous vehicle andseeking out additional power or power recharge for the autonomousvehicle.

The illustration of behavior database 600 in FIG. 6 is not meant toimply physical or architectural limitations to the manner in whichdifferent advantageous embodiments may be implemented. Other componentsin addition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

With reference now to FIG. 7, a block diagram of a worksite database isdepicted in accordance with an illustrative embodiment. Worksitedatabase 700 is an example of one implementation of worksite database308 in FIG. 3.

Worksite database 700 includes a number of databases processor unit 302of navigation system 300 may utilize when planning a path and/orcontrolling mobility system 310 in FIG. 3. Worksite database 700 mayinclude, without limitation, map database 702, landmark database 704,and/or any other suitable database of information for an autonomousvehicle.

Map database 702 includes number of worksite maps 706. Number ofworksite maps 706 may correspond to number of worksites 106 in FIG. 1,for example. In one illustrative embodiment, number of worksite maps 706may be loaded into map database 702 from a remote location, such as backoffice 102 in FIG. 1 using network 101. In another illustrativeembodiment, number of worksite maps 706 may be stored in map database702 after being generated by simultaneous localization and mappingprocess 334 in FIG. 3. In yet another illustrative embodiment, number ofworksite maps 706 may be loaded into map database 702 by a user, such asuser 108 in FIG. 1 over base system interface 318 and/or communicationsunit 304 in FIG. 3, for example. In an illustrative example,simultaneous localization and mapping process 334 in FIG. 3 may generatea worksite map during an initial operation in a worksite, and store theworksite map generated in map database 702 for later use in a futureoperation in the same worksite.

Number of worksite maps 706 may include, for example, withoutlimitation, worksite map 708, area coverage grid map 710, number ofworksite images 712, and/or any other suitable worksite map. Worksitemap 708 may be an a priori map stored in number of worksite maps 706,which includes landmark locations and obstacle information for aworksite, such as worksite 114 in FIG. 1, for example. Worksite map 708may be generated by a user, such as user 108 in FIG. 1 for example,identifying landmark locations and obstacles for a worksite on a mapand/or image of the worksite. In an illustrative example, worksite map708 may be used by autonomous vehicle 104 in FIG. 1 to plan an areacoverage path for the worksite, taking into account the landmarks andobstacles for the worksite.

Area coverage grid map 710 may be, for example, without limitation, aworksite map including an area coverage grid overlay, a worksite imageincluding an area coverage grid overlay, an area coverage grid for abounded space and/or worksite dimensions, and/or any other suitable areacoverage grid map. In an illustrative example, navigation system 300 inFIG. 3 may generate area coverage grid map 710 using worksite map 708provided by user 108 in FIG. 1. In another illustrative example,navigation system 300 may generate area coverage grid map 710 usinglandmark attribute information and obstacle information received from auser, such as user 108 in FIG. 1. In yet another illustrative example,autonomous vehicle 104 in FIG. 1 may acquire number of worksite images712 using a vision system, such as vision system 320 in FIG. 3, andgenerate area coverage grid map 710 using number of worksite images 712.

Landmark database 704 may include landmark attributes 714 and positioninformation 716. Landmark attributes 714 may include, for example,without limitation, landmark images, landmark definitions, landmarkcharacteristics, and/or any other suitable landmark attributes used toidentify a number of landmarks in a worksite, such as number oflandmarks 120 in worksite 114 in FIG. 1, for example. Landmark imagesmay include stored images of a number of different types of landmarks,for example. Landmark definitions may refer to names and/or descriptionsassociated with a number of landmarks, for example. Landmarkcharacteristics may include, for example, without limitation, shape,color, texture, and/or any other suitable characteristic for identifyinga number of landmarks. Position information 716 identifies the positionof a number of landmarks relative to locations within a worksiteidentified, such as worksite 114 in FIG. 1, for example. Positioninformation 716 may be associated with number of worksite maps 706stored in map database 702, for example.

The illustration of worksite database 700 in FIG. 7 is not meant toimply physical or architectural limitations to the manner in whichdifferent advantageous embodiments may be implemented. Other componentsin addition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

With reference now to FIG. 8, a block diagram of a worksite map isdepicted in accordance with an illustrative embodiment. Worksite map 800may be an illustrative example of one implementation of number ofworksite maps 706 in map database 702 of FIG. 7.

Worksite map 800 is generated for worksite 801. Worksite 801 may be anillustrative example of worksite 114 in FIG. 1. Worksite map 800includes landmark 802, landmark 804, and landmark 806. Worksite map 800also includes flower bed 808 and bush 810. In an illustrative example,flower bed 808 and bush 810 may be considered obstacles. Worksite map800 is defined by a perimeter on each side of the worksite, specificallyworksite boundary 812, worksite boundary 814, worksite boundary 816, andworksite boundary 818. A path plan may be generated for worksite map 800using sector decomposition process 332 in FIG. 3, for example.

The path plan may begin with starting point 820. The path plan proceedsfrom starting point 820 around landmark 802 until it reaches worksiteboundary 812. The path plan may maintain a predefined distance fromlandmark 802, creating an arc shaped path. The predefined distance maybe, for example, without limitation, a width of the autonomous vehiclefor which the path plan is being generated. Upon reaching worksiteboundary 812, the path plan follows worksite boundary 812 away fromlandmark 802 for the predefined distance. The path plan then proceedsback around landmark 802 until it reaches worksite boundary 814. Thepath plan maintains the predefined distance from each preceding arcshaped path. Upon reaching a worksite boundary, the path follows theworksite boundary the predefined distance away from the preceding arcshaped path before turning and proceeding back around the landmark, suchas landmark 802.

The path reaches an obstacle, in this example bush 810, at point A 822.The path is then made linear until it reaches worksite boundary 816 atpoint B 824. A next landmark is identified, in this example landmark804. The path proceeds around landmark 804, in concentric rings, untilit reaches point C 826. The path is then made linear until it reaches anobstacle or a worksite boundary, in this example flower bed 808 at pointD 828. Landmark 806 is identified and the path proceeds around landmark806 until it reaches point E 830. Point E 830 may be an illustrativeexample of a point reached where the autonomous vehicle following thepath is at a distance from landmark 806 at which landmark 806 is nolonger useful as a visual landmark. The distance may be such that therequired accuracy of image detection by a vision system of theautonomous vehicle is not met, for example. The autonomous vehicle maythen continue on a path around another landmark, even a previouslyvisited landmark, which is at a closer distance than landmark 806, forexample. At point E 830, the path again focuses on finishing a patharound landmark 802 on the opposite side of bush 810, where it hadpreviously left off to pursue a course around landmark 804. At point F832, the path again focuses on finishing a path around landmark 804,where it had previously left off upon encountering the perimeter whereworksite boundary 814 and worksite boundary 816 met and proceedinglinearly to point D 828. As the autonomous vehicle moves within theworksite, area coverage grid map 710 in FIG. 7 is updated to reflectwhich grids have been covered. The path continues in concentric ringsaround landmark 804 until it reaches the end and there are no additionallandmarks to visit and no additional area to cover for the worksite perarea coverage grid map 710.

An autonomous vehicle, such as autonomous vehicle 104 in FIG. 1, mayfollow the path plan generated for worksite 801 using worksite map 800.The autonomous vehicle may start at starting point 820 identified inworksite map 800. This section of the path from starting point 820around landmark 802 to worksite boundary 812 may be executed using asector decomposition behavior, such as follow arc 616 in FIG. 6. Whenthe autonomous vehicle reaches point A 822, the linear path to point B824 may be executed using a sector decomposition behavior, such aspoint-to-point 618 in FIG. 6.

The illustration of worksite map 800 in FIG. 8 is not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

With reference now to FIG. 9, a flowchart illustrating a process forexecuting a path plan is depicted in accordance with an illustrativeembodiment. The process in FIG. 9 may be implemented by a component suchas processor unit 302 of navigation system 300 in FIG. 3, for example.

The process begins by receiving a worksite map for a worksite having anumber of landmarks (step 902). The number of landmarks may bepositioned at the worksite so that at least one landmark is visible fromany location of the worksite. The number of landmarks may be positionedat the worksite by, for example, without limitation, a human, a robot,autonomously, naturally, and/or any other suitable method of landmarkplacement.

In an illustrative example, the worksite map may be an initial mapwithout a path plan, such as worksite map 708 in FIG. 7. The worksitemap may be retrieved from a map database, such as map database 702 inFIG. 7, or received from a user or back office, for example. In oneillustrative example, the worksite map may be an aerial image of theworksite in which obstacles, or boundaries, have been indicated by auser familiar with the worksite. The worksite map may also have markedlocations of landmarks for the worksite and landmark attributes, such asdiameter and color, marked by the user in this illustrative example.

The process generates an area coverage grid map having a number of areacoverage grid elements for the worksite using the worksite map (step904). The area coverage grid elements may be a number of sections of thearea coverage grid map, for example. In one illustrative example, anarea coverage grid map is generated from the worksite map, where thearea coverage grid map represents the same region as the worksite mapand is further divided into a grid. The size of each area coverage gridelement may be predefined and/or selected by a user. For example, eacharea coverage grid element may be between one tenth and twice the sizeof the autonomous vehicle slated to perform the area coverage task inthe worksite.

The process then generates a path plan for the worksite using theworksite map and the area coverage grid map (step 906). The processmarks the number of landmarks on the worksite map as ‘unvisited’ andinitializes the number of area coverage grid elements as ‘uncovered’(step 908). In one illustrative example, the worksite map is initializedby setting all designated landmarks as unvisited and the area coveragegrid map is initialized by setting all area coverage grid elements tozero. As the process proceeds, a landmark may be marked visited when allareas within a calculated distance of the landmark have been covered,for example. The calculated distance may be based on landmark size,vision system parameters, and/or a maximum acceptable distance errorbetween an autonomous vehicle and the landmark, for example.

In one illustrative example, an area is considered covered if apercentage of grid elements in the area have a coverage value greaterthan a given threshold value. The coverage value is the value of an areacoverage grid element. Starting from zero, the value is incremented byan amount each time the autonomous vehicle, or autonomous vehicleeffecter, is shown to be positioned at the area coverage grid elementuntil a value of at least one is achieved.

In one illustrative example, only zero or one values occur for coveragevalues, where zero indicates that the area coverage grid element is notcovered and one indicates that the area coverage grid element iscovered. In another illustrative example, error in autonomous vehiclelocalization may be considered in incrementing the area coverage gridelements. In this illustrative example, rather than setting the areacoverage grid element at the current calculated autonomous vehicleposition to one, a probability between zero and one is assigned to beingat that location and a lower probability to adjacent area coverage gridelements. The current and adjacent area coverage grid elements areincremented by the probability of occupancy. The sum of this currentprobability of occupancies adds up to one, in this illustrative example.

Next, the process performs an area coverage task at the worksite with anautonomous vehicle using the path plan (step 910). The processidentifies a landmark marked as unvisited on the worksite map (step912). The process sends a message to a vehicle control process to movethe autonomous vehicle to the landmark marked as unvisited (step 914).

The process executes an area coverage behavior on a path around thelandmark with the autonomous vehicle (step 916). The area coverage gridmap associated with the worksite, such as area coverage grid map 710 inFIG. 7, is updated based on each calculated current position of theautonomous vehicle used to execute the area coverage behavior. Theprocess then determines whether an obstacle is detected or a full circlehas been traversed by the autonomous vehicle (step 918). If adetermination is made that an obstacle has not been detected or a fullcircle has not been traversed, the process returns to step 916.

If a determination is made that an obstacle has been detected or a fullcircle has been traversed, the process determines whether the autonomousvehicle can move a given distance away from the landmark (step 920). Anautonomous vehicle may not be able to move the given distance away fromthe landmark due to an obstacle or because the calculated distance errorexceeds a threshold value, for example. If a determination is made thatthe autonomous vehicle can move the given distance away from thelandmark, the process sends a message to the vehicle control process tomove the autonomous vehicle the given distance away from the landmarkand execute the area coverage behavior in an opposite direction (step922), with the process then returning to step 918. If a determination ismade that the autonomous vehicle can not move the given distance awayfrom the landmark, the process marks the landmark as ‘visited’ on theworksite map (step 924). The process then determines whether there areany remaining landmarks marked as ‘unvisited’ on the worksite map (step926). If a determination is made that there are remaining landmarksmarked as ‘unvisited’ on the worksite map, the process identifies a nextlandmark marked as ‘unvisited’ on the worksite map (step 928) andreturns to step 914.

If a determination is made that there are no remaining landmarks markedas ‘unvisited’ on the worksite map, the process then determines whetherthere are any remaining area coverage grid elements marked as‘uncovered’ (step 930). If a determination is made that there areremaining area coverage grid elements marked as ‘uncovered’, the processsends a message to the vehicle control process to proceed along the pathplan to a visited landmark associated with an area coverage grid elementmarked as uncovered (step 932), and then returns to step 916. If adetermination is made that there are no remaining area coverage gridelements marked as ‘uncovered’, the process terminates thereafter.

With reference now to FIG. 10, a flowchart illustrating a process forexecuting a path plan using simultaneous localization and mapping isdepicted in accordance with an illustrative embodiment. The process inFIG. 10 may be implemented by a component such as simultaneouslocalization and mapping process 334 in FIG. 3, for example.

The process begins by receiving a number of landmark attributes andobstacle information for a worksite (step 1002) The landmark attributesmay be, for example, without limitation, landmark descriptions, images,characteristics, and/or any other suitable attribute. In oneillustrative example, the number of landmark attributes may identifylandmarks as cylinders with a given diameter and colors red, white, andblue.

The process generates an area coverage grid map having a number of gridelements (step 1004). The process then acquires an image of the worksite(step 1006). The image may be acquired using a vision system, such asvision system 320 in FIG. 3 using number of cameras 322, for example.The process determines whether a landmark is identified in the image(step 1008).

If a determination is made that that a landmark is not identified in theimage, the process searches for a landmark in the worksite area using anumber of cameras rotating at an amount which is the product of thefield of view in degrees multiplied by a value between zero and one toprovide image overlap in additional images acquired (step 1010). Theprocess then determines whether a landmark is identified in theadditional images acquired (step 1012). If a determination is made thata landmark is not identified in the additional images, the processdetermines whether the number of cameras have rotated 360 degrees (step1014). If a determination is made that the number of cameras haverotated 360 degrees, the process adds error handling (step 1016), andterminates thereafter. Error handling refers to the landmark rule, whichis that at least one landmark is always in view from all workableportions of a worksite. If at least one landmark cannot be found, therule is broken, and the process terminates.

If a determination is made that the number of cameras have not rotated360 degrees, the process returns to step 1010. If a determination ismade that a landmark is identified in the image in step 1008 or if adetermination is made that a landmark is identified in the additionalimages in step 1012, the process then determines if the landmarkidentified has been visited (step 1018). If a landmark has been visited,the area coverage grid map will be marked with a ‘visited’ landmarkpreviously identified.

If a determination is made that the landmark identified has beenvisited, the process determines whether all grid map elements have beencovered (step 1020). When a grid map element is covered, it will bemarked as ‘covered’ on the area coverage grid map. If there are areas ofthe area coverage grid map marked as ‘uncovered’ then there areremaining reachable grid map elements to cover. If a determination ismade that all grid map elements have been covered, the processterminates thereafter.

If a determination is made that all grid map elements have not beencovered, the process moves to a next worksite area and acquires a nextimage to look for landmarks (step 1022) and returns to step 1008.

If a determination is made that the landmark identified has not beenvisited, the process calculates a path plan to the landmark identified(step 1024). The process then marks the current position of anautonomous vehicle and estimated landmark position on the area coveragegrid map of the worksite (step 1026). The process executes the pathplan, marking the area coverage grid elements traversed as ‘covered’(step 1028), and proceeds to step 1020.

With reference now to FIG. 11, a flowchart illustrating a process forexecuting an area coverage path plan using sector decomposition isdepicted in accordance with an illustrative embodiment. The process inFIG. 11 may be implemented by a component such as navigation system 300in FIG. 3, for example.

The process begins by determining an expected width of a landmark inpixels for a desired distance from the landmark (step 1102). Theexpected width may be the width of a landmark expected to be identifiedin an image of the landmark at a given distance from the landmark. Theexpected width may be geometrically calculated based on the camera imageresolution for the number of cameras used to capture the image, theknown width of the landmark identified in a landmark database, thetarget distance of the autonomous vehicle from the landmark, and thefield of view for the number of cameras used to capture the image, forexample. The process identifies an image having the landmark (step1104). The image may be identified using a vision system, such as visionsystem 320 in FIG. 3, for example. The process filters the image to forma filtered image consisting of the landmark alone (step 1106). The imagemay be filtered to reduce pixel noise, for example. In one illustrativeexample, filtering may be accomplished optically using a polarizedwavelength selective filter on number of cameras 322 of vision system320, for example. In another illustrative example, wavelength selectivefiltering may be accomplished using software implemented in visionsystem 320. In yet another illustrative example, vision system 320 mayfilter number of images 324 in FIG. 3 by application of a median filterto remove pixel-level noise. The median filter may be a software processused by vision system 320 in this example.

The process optionally normalizes the orientation of cylindricallandmarks in the vertical direction in the filtered image (step 1108).The normalization of the image may be performed using vision system 320and/or processor unit 302 of FIG. 3, for example. In an illustrativeexample, if a landmark is a cylinder, the image may be processed toidentify the axis of the cylinder. The width is then calculatedorthogonal to the axis identified, in this example.

The process determines the observed width of the landmark in pixelsusing the filtered image (step 1110). In an illustrative example, theobserved width of the landmark may be calculated using a single crosssection of a normalized landmark from step 1108. In another illustrativeexample, the observed width of the landmark may be calculated by takingan average of a number of cross sections of the landmark identified inthe image. In an illustrative example where glare off a landmark isdetected, the number of cross section widths which are significantlylower than the majority or plurality of cross section widths may bedropped from the width calculation.

The process then determines whether the observed width is greater thanthe expected width (step 1112). If a determination is made that theobserved width is not greater than the expected width, the processdetermines whether the observed width is less than the expected width(step 1114). If a determination is made that the observed width is lessthan the expected width, the process sends a message to a vehiclecontrol process to turn an autonomous vehicle toward the landmark (step1116). If a determination is made that the observed width is not lessthan the expected width, the process determines whether a perimeter orobstacle is detected (step 1118).

If a determination is made that the observed width is greater than theexpected width, the process sends a message to the vehicle controlprocess to turn the autonomous vehicle away from the landmark (step1120) and proceeds to step 1118.

If a determination is made that a perimeter or obstacle is not detected,the process returns to step 1104. If a determination is made that aperimeter or obstacle is detected, the process terminates thereafter.

With reference now to FIG. 12, a flowchart illustrating a process forgenerating an area coverage path plan using sector decomposition isdepicted in accordance with an illustrative embodiment. The process inFIG. 12 may be implemented by a component such as navigation system 300in FIG. 3, for example.

The process begins by identifying a starting point on a worksite maphaving a number of landmarks (step 1202). The process identifies a firstlandmark in the number of landmarks (step 1204). The process begins apath from the starting point around the first landmark, maintaining apredefined distance from the first landmark to form a first arc (step1206). The process determines whether a worksite boundary is detected(step 1208).

If a determination is made that a worksite boundary is detected, theprocess moves the path a predefined width away from the first arc alongthe worksite boundary (step 1210). The process then continues the patharound the first landmark to form a next arc (step 1212), beforereturning to step 1208.

If a determination is made that a worksite boundary is not detected, theprocess determines whether an obstacle is detected (step 1214). If adetermination is made that no obstacle is detected, the process returnsto step 1206. If a determination is made that an obstacle is detected,the process makes the path linear to a vicinity of a next landmark (step1216). The process continues the path around the next landmark to form anumber of arcs (step 1218). The process iteratively repeats until thepath covers the worksite map (step 1220). The process then generates apath plan (step 1222), with the process terminating thereafter.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus, methods and computer programproducts. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of computer usable orreadable program code, which comprises one or more executableinstructions for implementing the specified function or functions. Insome alternative implementations, the function or functions noted in theblock may occur out of the order noted in the figures. For example, insome cases, two blocks shown in succession may be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The different advantageous embodiments can take the form of an entirelyhardware embodiment, an entirely software embodiment, or an embodimentcontaining both hardware and software elements. Some embodiments areimplemented in software, which includes but is not limited to forms,such as, for example, firmware, resident software, and microcode.

Furthermore, the different embodiments can take the form of a computerprogram product accessible from a computer-usable or computer-readablemedium providing program code for use by or in connection with acomputer or any device or system that executes instructions. For thepurposes of this disclosure, a computer-usable or computer readablemedium can generally be any tangible apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer usable or computer readable medium can be, for example,without limitation an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, or a propagation medium. Non limitingexamples of a computer-readable medium include a semiconductor or solidstate memory, magnetic tape, a removable computer diskette, a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk,and an optical disk. Optical disks may include compact disk-read onlymemory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.

Further, a computer-usable or computer-readable medium may contain orstore a computer readable or usable program code such that when thecomputer readable or usable program code is executed on a computer, theexecution of this computer readable or usable program code causes thecomputer to transmit another computer readable or usable program codeover a communications link. This communications link may use a mediumthat is, for example without limitation, physical or wireless.

A data processing system suitable for storing and/or executing computerreadable or computer usable program code will include one or moreprocessors coupled directly or indirectly to memory elements through acommunications fabric, such as a system bus. The memory elements mayinclude local memory employed during actual execution of the programcode, bulk storage, and cache memories which provide temporary storageof at least some computer readable or computer usable program code toreduce the number of times code may be retrieved from bulk storageduring execution of the code.

Input/output or I/O devices can be coupled to the system either directlyor through intervening I/O controllers. These devices may include, forexample, without limitation, keyboards, touch screen displays, andpointing devices. Different communications adapters may also be coupledto the system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Non-limiting examples ofmodems and network adapters are just a few of the currently availabletypes of communications adapters.

The description of the different advantageous embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different embodiments may providedifferent advantages as compared to other embodiments. The embodiment orembodiments selected are chosen and described in order to best explainthe principles of the invention, the practical application, and toenable others of ordinary skill in the art to understand the inventionfor various embodiments with various modifications as are suited to theparticular use contemplated.

1. A method for executing an area coverage path plan, the methodcomprising: determining an expected width of a landmark in pixels for adesired distance away from the landmark; identifying an image having thelandmark; determining an observed width of the landmark using the image;comparing the observed width of the landmark to the expected width ofthe landmark; and sending a message to a vehicle control process to movean autonomous vehicle based on the comparison of the observed width andthe expected width.
 2. The method of claim 1, further comprising:filtering the image to form a filtered image consisting of the landmark.3. The method of claim 1, further comprising: normalizing an orientationof the landmark in a vertical direction in the image.
 4. The method ofclaim 1, wherein comparing the observed width of the landmark to theexpected width of the landmark further comprises: determining whetherthe observed width of the landmark is greater than the expected width ofthe landmark; and responsive to a determination that the observed widthis greater than the expected width, sending a message to the vehiclecontrol process to move the autonomous vehicle away from the landmark.5. The method of claim 4, further comprising: responsive to adetermination that the observed width is not greater than the expectedwidth, determining whether the observed width is less than the expectedwidth; responsive to a determination that the observed width is lessthan the expected width, sending a message to the vehicle controlprocess to move the autonomous vehicle toward the landmark.
 6. Themethod of claim 5, further comprising: determining whether an obstacleis detected; and responsive to a determination that no obstacle isdetected, identifying a next image having a landmark.
 7. A method forexecuting a path plan, the method comprising: receiving a worksite mapfor a worksite having a number of landmarks; generating an area coveragegrid map having a number of area coverage grid elements for the worksiteusing the worksite map; generating a path plan for the worksite usingthe worksite map and the area coverage grid map; marking the number oflandmarks on the worksite map as unvisited; initializing the number ofarea coverage grid elements as uncovered; and performing an areacoverage task at the worksite.
 8. The method of claim 7, wherein theworksite map includes at least one of a number of landmark locations, anumber of landmark attributes, and a number of obstacles.
 9. The methodof claim 7, further comprising: identifying a landmark marked asunvisited on the worksite map; sending a message to a vehicle controlprocess to proceed to the landmark marked as unvisited; and executing anarea coverage behavior on a path around the landmark.
 10. The method ofclaim 9, further comprising: determining whether an obstacle isdetected; responsive to a determination that the obstacle is detected,marking the landmark marked as unvisited as visited on the worksite map;and marking associated area coverage grid elements of the landmarkmarked as visited as covered.
 11. The method of claim 7, furthercomprising: determining whether there are any remaining landmarks markedas unvisited; responsive to a determination that there are remaininglandmarks marked as unvisited, identifying a next landmark marked asunvisited on the worksite map; and sending a message to a vehiclecontrol process to proceed to the next landmark marked as unvisited. 12.The method of claim 11, further comprising: responsive to adetermination that there are no remaining landmarks marked as unvisited,determining whether there are any remaining area coverage grid elementsmarked as uncovered; responsive to a determination that there areremaining area coverage grid elements marked as uncovered, sending amessage to the vehicle control process to proceed along the path plan toa visited landmark associated with an area coverage grid element markedas uncovered; and executing an area coverage behavior on a path aroundthe visited landmark.
 13. A method for executing a path plan, the methodcomprising: receiving a number of landmark attributes and obstacleinformation for a worksite; generating an area coverage grid map havinga number of grid elements; acquiring an image of a worksite area of theworksite; determining whether a landmark is identified in the image;responsive to a determination that the landmark is identified in theimage, determining whether the landmark identified has been visited; andresponsive to a determination that the landmark identified has not beenvisited, calculating a path plan to the landmark identified.
 14. Themethod of claim 13, wherein the number of landmark attributes include atleast one of a landmark description, landmark image, and a landmarkcharacteristic.
 15. The method of claim 13, wherein the image of theworksite area is acquired using a vision system associated with anautonomous vehicle.
 16. The method of claim 13, further comprising:responsive to a determination that the landmark is not identified in theimage, searching for the landmark in the worksite area using a number ofcameras rotating at an amount which is the product of the field of viewin degrees multiplied by a value between zero and one to provide imageoverlap in additional images acquired.
 17. The method of claim 16,further comprising: determining wherein the landmark is identified inthe additional images acquired; responsive to a determination that thelandmark is not identified in the additional images acquired,determining whether the number of cameras have rotated 360 degrees; andresponsive to a determination that the number of cameras have notrotated 360 degrees, rotating the number of cameras at an amount whichis the product of the field of view in degrees multiplied by a valuebetween zero and one to provide image overlap in additional imagesacquired.
 18. The method of claim 17, responsive to a determination thatthe landmark is identified in the additional images acquired,determining whether the landmark identified has been visited; andresponsive to a determination that the landmark identified has not beenvisited, calculating a path plan to the landmark identified.
 19. Themethod of claim 13, further comprising: responsive to a determinationthat the landmark identified has been visited, determining whether allgrid map elements have been covered; responsive to a determination thatall grid map elements have not been covered, acquiring a next image fora next worksite area; and determining whether a next landmark isidentified in the next image.